2,105 research outputs found

    An adaptive envelope analysis in a wireless sensor network for bearing fault diagnosis using fast kurtogram algorithm

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    This paper proposes a scheme to improve the performance of applying envelope analysis in a wireless sensor network for bearing fault diagnosis. The fast kurtogram is realized on the host computer for determining an optimum band-pass filter for the envelope analysis that is implemented on the wireless sensor node to extract the low frequency fault information. Therefore, the vibration signal can be monitored over the bandwidth limited wireless sensor network with both intelligence and real-time performance. Test results have proved that the diagnostic information for different bearing faults can be successfully extracted using the optimum band-pass filter

    Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

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    Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate

    A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

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    Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs

    Diagnóstico de fallas en computación móvil usando TwinSVM

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    Introduction: Mobile computing systems (MCS) comes up with the challenge of low communication bandwidth and energy due to the mobile nature of the network. These features sometimes may come up with the undesirable behaviour of the system that eventually affects the efficiency of the system. Problem: Fault tolerance in MCS will increase the efficiency of the system even in the presence of faults. Objective: The main objective of this work is the development of the Monitoring Framework and Fault Detection and Classification. Methodology: For the Node Monitoring and for the detection and classification of faults in the system a neighbourhood comparison-based technique has been proposed. The proposed framework uses Twin Support Vector Machine (TWSVM) algorithm has been applied to build classifier for fault classification in the mobile network. Results: The proposed system has been compared with the existing techniques and has been evaluated towards calculating the detection accuracy, latency, energy consumption, packet delivery ratio, false classification rate and false positive rate. Conclusion: The proposed framework performs better in terms of all the selected parameters.Introducción: este artículo es el resultado de la investigación “Diagnóstico de fallas en la computación móvil usando TwinSVM” desarrollada en la Universidad Técnica I.K Gujral Punjab en Punjab, India en 2021.Problema: dado que los recursos en los sistemas informáticos móviles son limitados y un sistema tiene un ancho de banda, energía y movilidad de nodos limitados, el comportamiento deseado de la red puede cambiar si hay fallas.Objetivo: para lograr la tolerancia a fallas, de modo que un sistema móvil pueda operar incluso en presencia de fallas, se implementó un enfoque de dos temporizadores en el marco de detección, que luego se mejoró y perfeccionó con el uso del clasificador TwinSVM. Este clasificador ayuda a identificar nodos atípicos, lo que hace que el enfoque sea más tolerante a fallas.Metodología: el marco de monitoreo clasifica el nodo detectado como normal, defectuoso o parcialmente de-fectuoso, iniciando un temporizador de verificación de latidos y otro temporizador de verificación de relevancia en caso de que el nodo no responda al primer temporizador, que se prueba más usando TwinSVM, que mejora su eficiencia mediante la detección de valores atípicos.Resultados: el marco propuesto funciona mejor en términos de precisión de detección, consumo de energía, latencia y relación de caída de paquetes, todos los cuales han sido mejorados.Conclusión: el diagnóstico de fallas que utiliza el clasificador de aprendizaje automático TwinSVM funciona mejor en términos de falsas alarmas y tasas de falsos positivos y es adecuado para proporcionar tolerancia a fallas en sistemas informáticos móviles.Originalidad: a través de esta investigación, se ha desarrollado una versión única de detección de fallas en computación móvil utilizando un enfoque basado en clasificadores.Limitaciones: la falta de otras técnicas de detección de fallas cae dentro de la clasificación de fallas

    A fault fuzzy-ontology for large scale fault-tolerant wireless sensor networks

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    International audienceFault tolerance is a key research area for many of applications such as those based on sensor network technologies. In a large scale wireless sensor network (WSN), it becomes important to find new methods for fault-tolerance that can meet new application requirements like Internet of things, urbane intelligence and observation systems. The challenge is beyond the limit of a single wireless sensor network and concerns multiple widely interconnected sub networks. The domain of fault grows considerably because of this new configuration. In this context, the paper proposes a fault fuzzy-ontology (FFO) for large scale WSNs to be used within a Web service architecture for diagnosis and testing

    MEMS Accelerometers: Testing and Practical Approach for Smart Sensing and Machinery Diagnostics

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    In the recent years a major change in the engineering process of mechatronics and robotics has taken place. In various design oriented laboratories around the world a shift can be recognised from a focus on analysis, simulation and modelling combined with outsourcing hardware design to the use of digital fabrication tools (laser cutter, 3D printer) allowing a cyclic (iterative) design process inside in the lab. This chapter aims to give an overview of the impact of this change, using many examples from various projects, and will share some insights and lessons learned for facilitating and implementing this process

    Review of selection criteria for sensor and actuator configurations suitable for internal combustion engines

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    This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture
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